Predicting tourism demand by A.R.I.M.A. models

Petrevska, Biljana

doi:10.1080/1331677x.2017.1314822

Cited by 58 publications

(49 citation statements)

References 34 publications

(25 reference statements)

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“…In the ARIMA and SARIMA models, the behavior of a time series is explained from the past observations of the series itself and from the past forecasting errors. Several studies have shown how ARIMA models and their different variants obtain good results in forecasting tourism demand and, in most cases, surpass other time series methods, such as the periodic auto-regression model [32,33], moving averages [34], smoothed exponential [35], non-causal basic structural model [36], and multivariate adaptive regression splines [37], based on these authors who support the use of ARIMA models for predicting tourism demand, has served as an argument in this study to use the models in predicting ham tourism demand. These ARIMA models have the limitation of only detecting linear relationships and assume a probabilistic distribution of the data [38]; they do comply with the validation tests such as the Dickey-Fuller test for unit roots and with the absence of conditioned heteroscedasticity through ARCH (auto-regressive conditional heteroscedastic) models.…”

Section: Methodsologymentioning

confidence: 99%

Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile

2019

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: Iberian/serrano ham is a food product with a long tradition in the Spanish diet that is increasingly demanded by national and international consumers. Spain has 5 protected designations of origin (PDO) and two protected geographical indications (PGI) that support the quality of this product, which is manufactured only in the Iberian Peninsula. Since 2013, and based on the culinary product of ham, public entities and some businessmen have been trying to promote tourism related to ham, similar to wine-based tourism. In this study, we attempt to understand the profile of a ham tourist in the three existing routes in Andalusia, as well as ham tourism’s potential demand through seasonal auto-regressive integrated moving average (SARIMA) models. The results show that the profile of a ham tourist is similar to that of other typologies such as a tourist of wine or oil, and the demand has slow growth due to the novelty of this new tourism offering. Thus, there is a need to design strategies that allow the development of ham tourism, which will lead to an increase of wealth via employment in the local community.

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Section: Methodsologymentioning

confidence: 99%

Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile

2019

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“…The ARIMA method can be used to predict data that has nonseasonal and seasonal patterns. The ARIMA model for the seasonal data pattern is formulated as in [14] (6) ARIMA models can be used for short-run estimation based on annual, quarterly, monthly or even weekly, daily or hourly data as in [15]. If a good ARIMA model is obtained more than one model, then the best model can be determined using in-sample criteria and out-sample criteria.…”

Section: Arima Methodsmentioning

confidence: 99%

Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method

Ete¹,

Fitrianawati²,

Arifin³

2019

Proceedings of the First International Conference on Progressive Civil Society (ICONPROCS 2019)

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Singular Spectrum Analysis (SSA) is a time series method used to decompose the original time series into a sum of a small number of components that can be interpreted such as trends, oscillatory components, and noise. The purpose of this study is to compare the accuracy of the forecast between the SSA and ARIMA methods to obtain the best method in predicting the number of foreign tourist arrivals to Indonesia. The data used in this study is data on the number of arrival of foreign tourists to Indonesia through the Batam entrance. The forecasting results obtained using the SSA method will be compared with the ARIMA method to assess its superiority. The level of forecasting accuracy generated by each method is measured by the criteria of Mean Absolute Percentage Error (MAPE). The results of the study show that the ARIMA method produces better forecast accuracy than the SSA method for forecasting the number of tourist arrivals through the Batam's entrance. The MAPE value obtained from the results of forecasting using the ARIMA method is 9.83. The MAPE value obtained from the results of forecasting using the SSA method is 10.98.

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“…Four accuracy measurements were conducted to select the best model from Box-Jenkins approach. Otherwise, the second research by [17] used Macedonia tourism demand from 1956 until 2013 for forecasting. The researcher applied RMSE, MAE, APE, MAPE and TIC to choose forecasting model.…”

Section: Box-jenkins Approachmentioning

confidence: 99%

“…The researcher applied RMSE, MAE, APE, MAPE and TIC to choose forecasting model. For result comparison, model selected in [16] was SARIMA (0,1,1)(0,1,1)12 while model selected in [17] was ARIMA (1,1,1). Another two researches that applied Box-Jenkins approach in fisheries field can be found in [18] and [19].…”

Section: Box-jenkins Approachmentioning

confidence: 99%

Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

Hamzah¹,

Nor²,

Saharan³

et al. 2018

IJET

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Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.

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Predicting tourism demand by A.R.I.M.A. models

Cited by 58 publications

References 34 publications

Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile

Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile

Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method

Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

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